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Vehicle Pricing Model Data
Stock & Market Data
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About
This dataset is designed for car price prediction, a significant research area in machine learning. It provides crucial information for understanding how car prices depend on various factors, making it valuable for finance and marketing research. The dataset enables the assessment of vehicle depreciation, technology advancements, and wear and tear.
Columns
- Car_Name: Identifies the brand, company name, and specific model of each vehicle.
- Year: Indicates the manufacturing year of the vehicles, essential for evaluating depreciation and technological progress.
- Selling_Price: Represents the actual selling price of the car.
- Present_Price: Shows the current original price of the car.
- Kms_Driven: Provides the mileage of each vehicle, a key indicator of wear and tear and potential maintenance needs.
- Fuel_Type: Specifies the type of fuel the vehicles use, such as petrol or diesel.
- Seller_Type: Distinguishes whether the seller is a dealer or an individual.
- Transmission: Determines the transmission type, either automatic, manual, or another variant.
- Owner: Records the number of previous owners for the car (0, 1, 2, or 3).
Distribution
The dataset is provided as a CSV file named
car_prediction_data.csv, with a size of 17.21 kB. It consists of 9 columns and 301 valid records across all features, with no missing values reported.- Year: Ranges from 2003 to 2018, with the majority of vehicles manufactured between 2015 and 2016.50 (111 records).
- Selling_Price: Ranges from 0.1 to 35, with a mean of 4.66. Most cars are priced between 0.10 and 3.59 (150 records).
- Present_Price: Ranges from 0.32 to 92.6, with a mean of 7.63. Most cars have a present price between 0.32 and 9.55 (223 records).
- Kms_Driven: Ranges from 500 to 500,000 km, with a mean of 36,900 km. The majority of vehicles have driven between 500 and 50,450 km (236 records).
- Fuel_Type: Predominantly petrol (79%), followed by diesel (20%).
- Seller_Type: Mainly dealers (65%), with individuals making up 35%.
- Transmission: Mostly manual (87%), with automatic transmission at 13%.
- Owner: The majority of vehicles (290 out of 301) have 0 previous owners, with a range up to 3 owners.
Usage
This dataset is ideal for:
- Developing machine learning models for car price prediction.
- Conducting financial analysis related to vehicle depreciation.
- Performing market research to understand factors influencing car prices.
- Analysing the impact of mileage on wear and tear and maintenance requirements.
- Exploring the relationship between manufacturing year and technology advancements or depreciation.
- Building regression models for continuous variable prediction.
Coverage
The dataset covers vehicles manufactured over a time range from 2003 to 2018. Information on the number of previous owners is available, ranging from 0 to 3. The data does not specify any particular geographic or demographic scope beyond the vehicle attributes.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Machine Learning Researchers: For developing and testing car price prediction algorithms.
- Finance Professionals: To analyse vehicle asset valuation and depreciation trends.
- Marketing Analysts: For understanding market dynamics and pricing strategies in the automotive sector.
- Data Scientists: To build predictive models for automotive industry applications.
- Car Dealerships: To inform pricing strategies for used vehicles.
- Individuals: Interested in estimating the value of their cars or understanding influencing factors.
Dataset Name Suggestions
- Automobile Price Prediction Data
- Used Car Valuation Dataset
- Vehicle Pricing Model Data
- Car Depreciation Factors Data
Attributes
Original Data Source:Vehicle Pricing Model Data
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